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Statistics - Statistical Theory and Methods | Multivariate Statistics - Exercises and Solutions

Multivariate Statistics

Exercises and Solutions

Härdle, Wolfgang Karl, Hlávka, Zdenek

2nd ed. 2013, Approx. 400 p.

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ISBN 978-3-642-36005-3

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  • Goes further than most similar textbooks by considering SIR techniques that are not found typically in multivariate textbooks
  • Data sets discussed in the book can be downloaded and analyzed by every statistical package
  • Contains hundreds of solved exercises

The authors present tools and concepts of multivariate data analysis by means of exercises and their solutions. The first part is devoted to graphical techniques. The second part deals with multivariate random variables and presents the derivation of estimators and tests for various practical situations. The last part introduces a wide variety of exercises in applied multivariate data analysis. The book demonstrates the application of simple calculus and basic multivariate methods in real life situations. It contains altogether 234 solved exercises which can assist a university teacher in setting up a modern multivariate analysis course. All computer-based exercises are available in the R or XploRe languages. The corresponding libraries are downloadable from the Springer link web pages and from the author’s home pages.

Content Level » Graduate

Keywords » data analysis - factor analysis - factor model - high dimensional data analysis - multivariate distribution - multivariate statistics - principal component - regression models

Related subjects » Computational Intelligence and Complexity - Computational Science & Engineering - Database Management & Information Retrieval - Statistical Theory and Methods

Table of contents 

Part I Descriptive Techniques: Comparison of Batches.- Part II Multivariate Random Variables: A Short Excursion into Matrix Algebra.- Moving to Higher Dimensions.- Multivariate Distributions.- Theory of the Multinormal.- Theory of Estimation.- Hypothesis Testing.- Part III Multivariate Techniques: Regression Models.- Decomposition of Data Matrices by Factors.- Principal Component Analysis.- Factor Analysis.- Cluster Analysis.- Discriminant Analysis.- Correspondence Analysis.- Canonical Correlation Analysis.- Multidimensional Scaling.- Conjoint Measurement Analysis.- Applications in Finance.- Highly Interactive, Computationally Intensive Techniques.- Data Sets.

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